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Life History of the Arctic Squid Gonatus fabricii (Cephalopoda: Oegopsida) Reconstructed by Analysis of Individual Ontogenetic Stable Isotopic Trajectories

Cephalopods are important in Arctic marine ecosystems as predators and prey, but knowledge of their life cycles is poor. Consequently, they are under-represented in the Arctic ecosystems assessment models. One important parameter is the change in ecological role (habitat and diet) associated with in...

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Published in:Animals (Basel) 2022-12, Vol.12 (24), p.3548
Main Authors: Golikov, Alexey V, Ceia, Filipe R, Hoving, Hendrik J T, Queirós, José P, Sabirov, Rushan M, Blicher, Martin E, Larionova, Anna M, Walkusz, Wojciech, Zakharov, Denis V, Xavier, José C
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Language:English
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Summary:Cephalopods are important in Arctic marine ecosystems as predators and prey, but knowledge of their life cycles is poor. Consequently, they are under-represented in the Arctic ecosystems assessment models. One important parameter is the change in ecological role (habitat and diet) associated with individual ontogenies. Here, the life history of Gonatus fabricii, the most abundant Arctic cephalopod, is reconstructed by the analysis of individual ontogenetic trajectories of stable isotopes (δ13C and δ15N) in archival hard body structures. This approach allows the prediction of the exact mantle length (ML) and mass when the species changes its ecological role. Our results show that the life history of G. fabricii is divided into four stages, each having a distinct ecology: (1) epipelagic squid (ML < 20 mm), preying mostly on copepods; (2) epi- and occasionally mesopelagic squid (ML 20−50 mm), preying on larger crustaceans, fish, and cephalopods; (3) meso- and bathypelagic squid (ML > 50 mm), preying mainly on fish and cephalopods; and (4) non-feeding bathypelagic gelatinous females (ML > 200 mm). Existing Arctic ecosystem models do not reflect the different ecological roles of G. fabricii correctly, and the novel data provided here are a necessary baseline for Arctic ecosystem modelling and forecasting.
ISSN:2076-2615
2076-2615
DOI:10.3390/ani12243548